Journal ArticleDOI
New approach to vehicle license plate location based on new model YOLO-L and plate pre-identification
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TLDR
A new approach to vehicle license plate location based on new model YOLO-L and plate pre-identification and k-means++ clustering algorithm improves in two aspects to precisely locate the area of license plate.Abstract:
Currently, the conventional license plate location method fails to detect the license plate under complex road environments such as severe weather conditions and viewpoint changes. Besides, it is difficult for license plate location method based on machine learning to precisely locate the area of license plate. Moreover, license plate location method may incorrectly detect similar objects such as billboards and road signs as license plates. To alleviate these problems, this article proposes a new approach to vehicle license plate location based on new model YOLO-L and plate pre-identification. The new model improves in two aspects to precisely locate the area of license plate. First, it uses k-means++ clustering algorithm to select the best number and size of plate candidate boxes. Second, it modifies the structure and depth of YOLOv2 model. Plate pre-identification algorithm can effectively distinguish license plates from similar objects. The experimental results show that authors' proposed method not only achieves a precision of 98.86% and a recall of 98.86%, which outperforms the existing methods, but also has high efficiency in real time.read more
Citations
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Ratio-and-Scale-Aware YOLO for Pedestrian Detection
Wei-Yen Hsu,Wen-Yen Lin +1 more
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Detecting Motion Blurred Vehicle Logo in IoV Using Filter-DeblurGAN and VL-YOLO
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Deep convolutional neural network for enhancing traffic sign recognition developed on Yolo V4
TL;DR: Experiments show that Yolo V4_1 (with SPP) outperforms the state-of-the-art schemes, achieving 99.4% accuracy in the authors' experiments, along with the best total BFLOPS and mAP (99.32%) in their experiment, and SPP can enhance the achievement of all models in the experiment.
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Automatic detection of oil palm fruits from UAV images using an improved YOLO model
TL;DR: In this paper, an improved YOLO model was proposed to detect oil palm loose fruits from UAV images, where the images are augmented by brightness, rotation, and blurring to simulate the actual natural environment.
References
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